
We propose a versatile approach which employs the same learned prior model for solving various computational imaging problems.

We propose to use a deep autoregressive model, PixelCNN++, as an image prior. The autoregressive nature of this prior ensures pixel-level consistencies in the reconstruction and hence provides better quality than using latent representation based models such as OneNet [1]} as shown in Figure REF .

We utilize back-propagation to the inputs for obtaining tractable estimates of the prior gradients and employ them for solving inverse problems using MAP inference.

We observe that randomly dropping the gradient updates for a certain percentage of pixels at every iteration helps in reconstructing the texture better. We analyze the effect of this pixel dropout ratio on the quality of reconstructions.

We demonstrate better reconstructions than the existing state-of-the-art methods for three computational imaging problems: Single Pixel Camera, LiSens, and FlatCam.

